5 citations · 6 across the 5 of their papers we have counts for
6 papers
Neuroverse3D: Developing In-Context Learning Universal Model for Neuroimaging in 3D
Jiesi Hu, Chenfei Ye, Yanwu Yang +5
In-context learning (ICL), a type of universal model, demonstrates exceptional generalization across a wide range of tasks without retraining by leveraging task-specific guidance f…
BrainMass: Advancing Brain Network Analysis for Diagnosis with Large-scale Self-Supervised Learning
Yanwu Yang, Chenfei Ye, Guinan Su +6
Foundation models pretrained on large-scale datasets via self-supervised learning demonstrate exceptional versatility across various tasks. Due to the heterogeneity and hard-to-col…
NexToU: Efficient Topology-Aware U-Net for Medical Image Segmentation
Pengcheng Shi, Xutao Guo, Yanwu Yang +2
Convolutional neural networks (CNN) and Transformer variants have emerged as the leading medical image segmentation backbones. Nonetheless, due to their limitations in either prese…
Accelerating Diffusion Models via Pre-segmentation Diffusion Sampling for Medical Image Segmentation
Xutao Guo, Yanwu Yang, Chenfei Ye +3
Based on the Denoising Diffusion Probabilistic Model (DDPM), medical image segmentation can be described as a conditional image generation task, which allows to compute pixel-wise…
Multi-modal Dynamic Graph Network: Coupling Structural and Functional Connectome for Disease Diagnosis and Classification
Yanwu Yang, Xutao Guo, Zhikai Chang +3
Multi-modal neuroimaging technology has greatlly facilitated the efficiency and diagnosis accuracy, which provides complementary information in discovering objective disease biomar…
Estimating Brain Age with Global and Local Dependencies
Yanwu Yang, Xutao Guo, Zhikai Chang +4
The brain age has been proven to be a phenotype of relevance to cognitive performance and brain disease. Achieving accurate brain age prediction is an essential prerequisite for op…